Precise PEM fuel cell parameter extraction based on a self-consistent model and SCCSA optimization algorithm

[Display omitted] •A precise modified modeling of PEMFC based on electrochemical phenomena is proposed.•A very common flaw in the previous PEMFC parameter extraction studies is detected.•A new application of SCCSA algorithm is presented for PEMFC parameter extraction.•The proposed model is validated using existing experimental data of six PEMFCs. Mathematical modeling of a polymer electrolyte membrane fuel cell (PEMFC) is widely used for investigating its performance. The development of a precise model is crucial for achieving a consistent and accurate simulation of PEMFC performance. Although many studies have been conducted to address the simulation of the characteristics of PEMFC by identifying the uncertain model parameters, they overwhelmingly suffer from a physical flaw, which invalidates their reported results. The aims of the present study are twofold: (1) to highlight the critical inconsistency in the previous research works, and (2) to develop an accurate, self-consistent model, which prevents yielding physically flawed results that are frequently seen in the open literature. The newly proposed model is then used in a coalition with a recent optimization algorithm called hybrid sine-cosine crow search algorithm (SCCSA). Using existing experimental data, the accuracy and efficiency of the proposed formulation are evaluated. Our results indicate good agreement between experimental and computed data for all the test cases, which in return demonstrates the decisive accuracy and usefulness of the developed model. A comparison between the obtained fitness values of this study and the ones reported in the literature shows the superiority of the proposed model. Furthermore, the capability of SCCSA algorithm is examined by comparing the obtained results with those of other meta-heuristic optimization algorithms. Performance comparison across SCCSA and other algorithms, such as particle swarm optimization (PSO), proves the adequate capability of this method to find the optimal fitness and its high convergence speed.